The large tech companies – Google, Meta/Facebook, Microsoft – are in a race to introduce new artificial intelligence systems and what are called chatbots, that you can have conversations with and are more sophisticated than Siri or Alexa. Microsoft's AI search engine and chatbot, Bing, can be used on a computer or cell phone to help with planning a trip or composing a letter. It was introduced on February 7 to a limited number of people as a test – and initially got rave reviews. But then several news organizations began reporting on a disturbing so-called "alter ego" within Bing Chat, called Sydney. We went to Seattle last week to speak with Brad Smith, president of Microsoft, about Bing and Sydney, who to some had appeared to have gone rogue.

Learning user preferences for products based on their past purchases or reviews is at the cornerstone of modern recommendation engines. One complication in this learning task is that some users are more likely to purchase products or review them, and some products are more likely to be purchased or reviewed by the users. This non-uniform pattern degrades the power of many existing recommendation algorithms, as they assume that the observed data are sampled uniformly at random among user-product pairs. In addition, existing literature on modeling non-uniformity either assume user interests are independent of the products, or lack theoretical understanding. In this paper, we first model the user-product preferences as a partially observed matrix with non-uniform observation pattern. Next, building on the literature about low-rank matrix estimation, we introduce a new weighted trace-norm penalized regression to predict unobserved values of the matrix. We then prove an upper bound for the prediction error of our proposed approach. Our upper bound is a function of a number of parameters that are based on a certain weight matrix that depends on the joint distribution of users and products. Utilizing this observation, we introduce a new optimization problem to select a weight matrix that minimizes the upper bound on the prediction error. The final product is a new estimator, NU-Recommend, that outperforms existing methods in both synthetic and real datasets. Our approach aims at accurate predictions for all users while prioritizing fairness. To achieve this, we employ a bias-variance tradeoff mechanism that ensures good overall prediction performance without compromising the predictive accuracy for less active users.

We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts. Two types of diffusion models, a generator model, which generates an intermediate representation conditioned on text, and a cascader model, which generates high-fidelity audio conditioned on the intermediate representation and possibly the text, are trained and utilized in succession to generate high-fidelity music. We explore two options for the intermediate representation, one using a spectrogram and the other using audio with lower fidelity. We find that the generated audio is not only able to faithfully reflect key elements of the text prompt such as genre, tempo, instruments, mood, and era, but goes beyond to ground fine-grained semantics of the prompt. Pretrained large language models play a key role in this story -- they are used to generate paired text for the audio of the training set and to extract embeddings of the text prompts ingested by the diffusion models. Generated examples: https://google-research.github.io/noise2music

Microsoft is enabling Video Super Resolution for their Edge browser. The video upscaling feature is now available to GeForce and Radeon users. Microsoft confirms that all RTX models (20, 30 and 40 series with Tensor cores) and Radeon RX 5700-7800 series will support this technology. The AI technology will only work if the video resolution is lower than 720p but not smaller than 192 pixels wide or high. Furthermore, it only works for video content that is not protected by DRM (PlayReady or Widevine), so it may not work for sites such as Netflix or other streaming services. Microsoft’s VSR technology is said to remove compression artifacts and enhance text crispiness. The feature is now available to 50% of users and will be enabled for the rest later. However, users can now enable this feature manually by changing the browser’s flag: edge://flags/#edge-video-super-resolution Another condition that must be met is AC power, it will not

Can you guess the parents? A digital artist has used Artificial Intelligence to merge the faces of two TV celebrities to create lifelike images of their possible children. The images are a result of the imagination of Brazilian digital artist Hidreley Leli Dião. The artist created these super-realistic creations imagining what the love children of two TV stars would have looked like if their romance in the TV shows or films they starred in progressed. So, MailOnline asks, can you work out the parents that these children belong to? Some are harder than others - but we've started you off pretty soft.

Companies from Meta to Home Depot are flooding earnings calls with commentary about their artificial intelligence efforts. Ever since OpenAI's ChatGPT lit up the internet in November, companies can't stop talking about artificial intelligence. Take this earnings season so far: References to AI and related terms during calls with investors are already up 77% from a year earlier. AI-hungry investors have propelled Nvidia Corp., which makes the chips needed for complex AI computing tasks, into the best-performing stock among mega-caps this year. Relatively obscure firms with AI in their names have also skyrocketed.

Large language models like ChatGPT are purveyors of plausibility. The chatbots, many based on so-called generative AI, are trained to respond to user questions by scraping the internet for relevant information and assembling coherent answers, churning out convincing student essays, authoritative legal documents and believable news stories. But, because publicly available data contains misinformation and disinformation, some machine-generated texts might not be accurate or true. That has triggered a scramble to develop tools to identify whether text has been drafted by human or machine. Science is also struggling to adjust to this new era, with live discussions over whether chatbots should be allowed to write scientific papers or even generate new hypotheses.

Machine learning practitioners often end up tunneling on low-level technical details like model architectures and performance metrics. Could early model development instead focus on high-level questions of which factors a model ought to pay attention to? Inspired by the practice of sketching in design, which distills ideas to their minimal representation, we introduce model sketching: a technical framework for iteratively and rapidly authoring functional approximations of a machine learning model's decision-making logic. Model sketching refocuses practitioner attention on composing high-level, human-understandable concepts that the model is expected to reason over (e.g., profanity, racism, or sarcasm in a content moderation task) using zero-shot concept instantiation. In an evaluation with 17 ML practitioners, model sketching reframed thinking from implementation to higher-level exploration, prompted iteration on a broader range of model designs, and helped identify gaps in the problem formulation$\unicode{x2014}$all in a fraction of the time ordinarily required to build a model.

Is it possible for machines to think like humans? And if it is, how should we go about teaching them to do so? As early as 1950, Alan Turing stated that we ought to teach machines in the way of teaching a child. Reinforcement learning with human feedback (RLHF) has emerged as a strong candidate toward allowing agents to learn from human feedback in a naturalistic manner. RLHF is distinct from traditional reinforcement learning as it provides feedback from a human teacher in addition to a reward signal. It has been catapulted into public view by multiple high-profile AI applications, including OpenAI's ChatGPT, DeepMind's Sparrow, and Anthropic's Claude. These highly capable chatbots are already overturning our understanding of how AI interacts with humanity. The wide applicability and burgeoning success of RLHF strongly motivate the need to evaluate its social impacts. In light of recent developments, this paper considers an important question: can RLHF be developed and used without negatively affecting human societies? Our objectives are threefold: to provide a systematic study of the social effects of RLHF; to identify key social and ethical issues of RLHF; and to discuss social impacts for stakeholders. Although text-based applications of RLHF have received much attention, it is crucial to consider when evaluating its social implications the diverse range of areas to which it may be deployed. We describe seven primary ways in which RLHF-based technologies will affect society by positively transforming human experiences with AI. This paper ultimately proposes that RLHF has potential to net positively impact areas of misinformation, AI value-alignment, bias, AI access, cross-cultural dialogue, industry, and workforce. As RLHF raises concerns that echo those of existing AI technologies, it will be important for all to be aware and intentional in the adoption of RLHF.

Silent speech interface is a promising technology that enables private communications in natural language. However, previous approaches only support a small and inflexible vocabulary, which leads to limited expressiveness. We leverage contrastive learning to learn efficient lipreading representations, enabling few-shot command customization with minimal user effort. Our model exhibits high robustness to different lighting, posture, and gesture conditions on an in-the-wild dataset. For 25-command classification, an F1-score of 0.8947 is achievable only using one shot, and its performance can be further boosted by adaptively learning from more data. This generalizability allowed us to develop a mobile silent speech interface empowered with on-device fine-tuning and visual keyword spotting. A user study demonstrated that with LipLearner, users could define their own commands with high reliability guaranteed by an online incremental learning scheme. Subjective feedback indicated that our system provides essential functionalities for customizable silent speech interactions with high usability and learnability.